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Item Details
| Title:
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CONNECTIONIST APPROACHES TO LANGUAGE LEARNING
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| By: |
David S. Touretzky (Editor) |
| Format: |
Hardback |
| List price:
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£117.00 |
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We currently do not stock this item, please contact the publisher directly for
further information.
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| ISBN 10: |
0792392167 |
| ISBN 13: |
9780792392163 |
| Publisher: |
SPRINGER |
| Pub. date: |
30 September, 1991 |
| Edition: |
Reprinted from Machine Learning, Volume 7:2/3 |
| Series: |
The Springer International Series in Engineering and Computer Science 154 |
| Pages: |
149 |
| Synopsis: |
arise automatically as a result of the recursive structure of the task and the continuous nature of the SRN's state space. Elman also introduces a new graphical technique for study- ing network behavior based on principal components analysis. He shows that sentences with multiple levels of embedding produce state space trajectories with an intriguing self- similar structure. The development and shape of a recurrent network's state space is the subject of Pollack's paper, the most provocative in this collection. Pollack looks more closely at a connectionist network as a continuous dynamical system. He describes a new type of machine learning phenomenon: induction by phase transition. He then shows that under certain conditions, the state space created by these machines can have a fractal or chaotic structure, with a potentially infinite number of states. This is graphically illustrated using a higher-order recurrent network trained to recognize various regular languages over binary strings. Finally, Pollack suggests that it might be possible to exploit the fractal dynamics of these systems to achieve a generative capacity beyond that of finite-state machines. |
| Illustrations: |
IV, 149 p. |
| Publication: |
Netherlands |
| Imprint: |
Springer |
| Returns: |
Returnable |
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